code: def binomial(
      random_state,
      n,
      p,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a binomial distribution.
  
      Samples are drawn from a binomial distribution with specified
      parameters, n trials and p probability of success where
      n an integer >= 0 and p is in the interval [0,1]. (n may be
      input as a float, but it is truncated to an integer in use)
  
      Parameters
      ----------
      n : int or array_like of ints
          Parameter of the distribution, >= 0. Floats are also accepted,
          but they will be truncated to integers.
      p : float or array_like of floats
          Parameter of the distribution, >= 0 and <=1.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``n`` and ``p`` are both scalars.
          Otherwise, ``mt.broadcast(n, p).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized binomial distribution, where
          each sample is equal to the number of successes over the n trials.
  
      See Also
      --------
      scipy.stats.binom : probability density function, distribution or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the binomial distribution is
  
      .. math:: P(N) = \binom{n}{N}p^N(1-p)^{n-N},
  
      where :math:`n` is the number of trials, :math:`p` is the probability
      of success, and :math:`N` is the number of successes.
  
      When estimating the standard error of a proportion in a population by
      using a random sample, the normal distribution works well unless the
      product p*n <=5, where p = population proportion estimate, and n =
      number of samples, in which case the binomial distribution is used
      instead. For example, a sample of 15 people shows 4 who are left
      handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,
      so the binomial distribution should be used in this case.
  
      References
      ----------
      .. [1] Dalgaard, Peter, "Introductory Statistics with R",
             Springer-Verlag, 2002.
      .. [2] Glantz, Stanton A. "Primer of Biostatistics.", McGraw-Hill,
             Fifth Edition, 2002.
      .. [3] Lentner, Marvin, "Elementary Applied Statistics", Bogden
             and Quigley, 1972.
      .. [4] Weisstein, Eric W. "Binomial Distribution." From MathWorld--A
             Wolfram Web Resource.
             http://mathworld.wolfram.com/BinomialDistribution.html
      .. [5] Wikipedia, "Binomial distribution",
             http://en.wikipedia.org/wiki/Binomial_distribution
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> n, p = 10, .5  # number of trials, probability of each trial
      >>> s = mt.random.binomial(n, p, 1000).execute()
      # result of flipping a coin 10 times, tested 1000 times.
  
      A real world example. A company drills 9 wild-cat oil exploration
      wells, each with an estimated probability of success of 0.1. All nine
      wells fail. What is the probability of that happening?
  
      Let's do 20,000 trials of the model, and count the number that
      generate zero positive results.
  
      >>> (mt.sum(mt.random.binomial(9, 0.1, 20000) == 0)/20000.).execute()
      # answer = 0.38885, or 38%.
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .binomial(handle_array(n), handle_array(p), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorBinomial(seed=seed, size=size, gpu=gpu, dtype=dtype)
      return op(n, p, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/binomial.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: binomial(random_state, n, p, size, chunk_size, gpu, dtype) and returns: op